class_docstr: Class for the manifold of beta distributions.
  
  This is Beta = :math:`R_+^* \times R_+^*`, the upper-right
  quadrant of the Euclidean plane.
  
  Attributes
  ----------
  dim : int
      Dimension of the manifold of beta distributions, equal to 2.
  embedding_space : Manifold
      Embedding manifold.
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docstr: Compute pdf associated to point.
  
  Compute the probability density function of the beta
  distribution with parameters provided by point.
  
  Parameters
  ----------
  point : array-like, shape=[..., 2]
      Point representing a beta distribution.
  
  Returns
  -------
  pdf : function
      Probability density function of the beta distribution with
      parameters provided by point.
docstr_tok: ["Compute","pdf","associated","to","point",".","Compute","the","probability","density","function","of","the","beta","distribution","with","parameters","provided","by","point",".","Parameters","--","--","--","--","--","point",":","array-like",",","shape=","[","...",",","2","]","Point","representing","a","beta","distribution",".","Returns","--","--","--","-","pdf",":","function","Probability","density","function","of","the","beta","distribution","with","parameters","provided","by","point","."]
code: def point_to_pdf(self, point):
      """Compute pdf associated to point.
  
      Compute the probability density function of the beta
      distribution with parameters provided by point.
  
      Parameters
      ----------
      point : array-like, shape=[..., 2]
          Point representing a beta distribution.
  
      Returns
      -------
      pdf : function
          Probability density function of the beta distribution with
          parameters provided by point.
      """
      alpha = gs.expand_dims(point[..., 0], axis=-1)
      beta = gs.expand_dims(point[..., 1], axis=-1)
  
      def pdf(x):
          """Generate parameterized function for normal pdf.
  
          Parameters
          ----------
          x : array-like, shape=[n_samples,]
              Points at which to compute the probability density function.
  
          Returns
          -------
          pdf_at_x : array-like, shape=[..., n_samples]
              Values of pdf at x for each value of the parameters provided
              by point.
          """
          x = gs.reshape(gs.array(x), (-1,))
          return (
              x ** (alpha - 1)
              * (1 - x) ** (beta - 1)
              / (
                  gs.gamma(alpha)
                  * gs.gamma(beta)
                  / gs.gamma(alpha + beta)
              )
          )
  
      return pdf
  
file_path: geomstats/geomstats/information_geometry/beta.py
imports: from scipy.stats import beta
  import geomstats.backend
  from geomstats.information_geometry.base import ScipyUnivariateRandomVariable
  from geomstats.information_geometry.dirichlet import DirichletDistributions, DirichletMetric
label_desc: Use image node_id 4 for calling the BetaDistributions obj's underlying member method code with example usage: obj.point_to_pdf(point) and returns: pdf, unknown